1. 3: L model evaluation: Łódź (2001-02) & Baltimore (2002-06) (Loridan et al. 2010) 2: A simple parameterization of incoming longwave radiation (Loridan et al. 2010) 2.1: Background 2.2: Cloud fraction estimate from ceilometer data at Kings College London Urban Meteorology Group, Kings College London http://geography.kcl.ac.uk/micromet/index.htm Contact: thomas.loridan@kcl.ac.uk Introduction Local-scale Urban Meteorological Parameterization Scheme (LUMPS, Grimmond and Oke 2002), a simple model to simulate the heat fluxes from the urban environment, that only requires easily obtained meteorological variables and surface characteristics. Objectives: (1) Parameterization of incoming longwave radiation is revisited using cloud data from the Kings College London urban field site (2) Performance of LUMPS is evaluated using independent data from the Łódź field campaign (Offerle et al., 2006) 1: Overview of LUMPS Thomas Loridan*, Sue Grimmond*, Brian D. Offerle**, Duick T. Young*, Thomas E. L. Smith*, Leena Järvi* and Fredrik Lindberg* *Kings College London, Department of Geography, London, UK, ** FluxSense, Goteborg, Sweden University of Helsinki, Department of Physics, Helsinki, Finland References Crawford, B., C.S.B. Grimmond, J. Hom, and A. Christen, 2010: Carbon dioxide fluxes in suburban Baltimore: 2002 – 2006. (in preparation) Grimmond, C. S. B., and T. R. Oke, 2002: Turbulent heat fluxes in urban areas: observations and a local-scale urban meteorological parameterization scheme (LUMPS). Journal of Applied Meteorology 41: 792–810. Loridan, T., C. S. B. Grimmond, B. Offerle, D. T. Young, T. E. L. Smith, L. Järvi, and F. Lindberg, 2010: Local-Scale Urban Meteorological Parameterization Scheme (LUMPS): longwave radiation parameterization and seasonality related developments. Journal of Applied Meteorology. (submitted) Offerle, B., C. S. B. Grimmond, and T. R. Oke, 2003: Parameterization of net all-wave radiation for urban areas. Journal of Applied Meteorology 42: 1157–1173. Offerle, B., C. S. B. Grimmond, K. Fortuniak, K. Klysik, and T. R. Oke, 2006: Temporal variations in heat fluxes over a central European city centre. Theoretical and applied climatology 84: 103-116. Acknowledgments Thanks to all those who were involved in the Baltimore, Łódź and London field campaigns, especially Krzysztof Fortuniak, Alastair Reynolds, and Steve Scott. Financial support for this project for the fieldwork and the analyses was provided to Grimmond by the U. S. National Science Foundation (ATM-0710631, BCS-0221105, BCS-0095284), EU (FP7-ENV-2007-1 211345) Bridge, USDA Forest Service (CA-11242343-082; 04-CA-11242343-124; 05-CA-11242343-11), and Kings College London. The LUMPS model is available at: http://geography.kcl.ac.uk/micromet/index.htm Sub- model Inputs and parametersParameterization scheme NARP (Offerle et al. 2003) K (from measurements) L (measured or modelled) T a : air temperature (measured) α 0, ε 0 : bulk surface albedo and emissivity OHM Q* (from NARP, eq. 1) a 1i, a 2i, a 3i : OHM surface characteristics f i : fraction area cover Q H / Q E partitioning scheme Q* (from NARP, eq. 1) ΔQ S (from OHM, eq. 2) s: slope of the saturation vapour pressure- temperature curve (Pa K -1 ) γ: psychrometric constant (Pa K -1 ) α, β: partitioning coefficients (depends on surface cover, Table 4) LUMPS simulates the surface energy balance (SEB) fluxes in a succession of sub-models (Fig. 1, Table 1): [SEB]Q* (+Q F ) = Q H + Q E + Q S where Q* net all-wave radiation and consists of: Q* = K - K + L - L where K (K) incoming (outgoing) shortwave radiation L (L) incoming (outgoing) longwave radiation Q F anthropogenic heat flux (not explicitly modelled) Q H turbulent sensible heat Q E turbulent latent heat flux Q S net heat storage flux TheoryParameterizationNotation L considered to be emitted by a single-layer atmosphere (Stefan-Boltzmann law) (4) ε sky : Sky emissivity T sky : Bulk sky temperature (K) σ: Stefans constant (W m -2 K -4 ) Clear sky emissivity (ε clear ): approximated from Prata (1996) using commonly measured meteorological quantities (5) ε clear : Clear sky emissivity w: precipitable water content (g cm -2 ) T a : measured air temperature (K) e a : measured vapour pressure (hPa) Impact of clouds on L: all sky emissivity (ε sky ) derived from an estimation of cloud fraction (F CLD ) T sky is approximated by T a (6) F CLD : Cloud fraction (0<F CLD <1) F CLD from observation (see 2.2) or modelled from other meteorological quantities (e.g. Section 2.3) 2.3: Impact of cloud on L 3.1: Model set-up Five options to model L are implemented in LUMPS: 1. User provided L (e.g. from direct observations) 2. Modelled using observation of cloud fraction 3. Modelled as in section 2.3 4. Modelled from observations of K as in original NARP 5. Modelled combined options 3 and 4 Performance: Root Mean Square Error (RMSE) and Mean Bias Error (MBE) presented in Fig. 4 for Baltimore (Crawford et al., 2010) and Łódź (Offerle et al. 2006) datasets. Cloud observation for option 2 were obtained from the U.S. National Climatic Data Center (NCDC) database. Conclusions A simple parameterization of incoming longwave radiation based on relative humidity and air temperature was developed using cloud data from a ceilometer in central London. The new parameterization was implemented in LUMPS and its impact on the modelling of the surface energy balance fluxes was evaluated using field observations from Łódź, Poland. There is good overall performance of the scheme for all fluxes and seasons. Detailed analysis shows model errors are a function of air temperature and wind direction. Better results are obtained when anthropogenic heat flux and flux variability from different source areas are taken into account. Model input parametersValues assigned for the Łódź runs Bulk albedo (α 0 ) emissivity (ε 0 ) α 0 = 0.08 ε 0 =0.92 Latitude (lat) Longitude (lon) lat=51.75 o N lon= 19.46 o E Number of surface types in OHM formulation (n in eq. 2) n=3 Fraction cover of each surface type: f build = 0.3 buildings f imp = 0.4 impervious f veg = 0.3 vegetated OHM empirical coefficients (Table 1, eq. 2) a 1veg =0.11, a 2veg =0.11 h, a 3veg = -12.3 W m -2 Vegetation: Mixed forest (McCaughey 1985) a 1build =0.06, a 2build =0.28 h, a 3build = -3 W m -2 Roof: Bitumen spread over flat industrial membrane (Meyn and Oke, 2009) a 1imp =0.7, a 2imp =0.33 h, a 3imp = -38.28 W m -2 Impervious: Mean of all 5 concrete and asphalt sources (Table 4, Grimmond and Oke, 1999) Reservoir capacity (res cap ) Drainage rate (res drain ) Threshold for complete surface coverage (raincover) res cap = 10 mm res drain = 0.25 mm h -1 raincover = 0.01 mm Vegetation Phenology (Table 3, eq. 9) s start =69 s stop =144; f start =281 f stop =324 V 0 =0.03 α int, α slope, β int and β slope (Table 1, eq. 3) (1) (2) (3) Table 1: Summary of three sub-models composing LUMPS Figure 1: Overview of the LUMPS model Common meteorological variables considered as potential predictors for the hourly averaged F CLD data (see 2.2): air temperature (K), relative humidity (%), vapour pressure and vapour pressure deficit (hPa), precipitable water content (g cm -2 ), specific humidity (kg kg -1 ) and the cooling rate of the air (K s -1 ). Stepwise selection process designed to identify the predictors with the largest correlation with F CLD First predictor selected: relative humidity (RH), then air temperature (T a ) Locally weighted polynomial regression (lowess): applied to F CLD and RH measurements (Fig. 3) to identify dominant trend Nonlinear regression use to approximate the lowess curve: Evolution of non-linear regression curve with increasing temperatures (not shown): coefficient in the exponential is allowed to evolve as a function of T a. To avoid systematic biases at very low humidity level (e.g. F CLD 0 if RH = 0): parameterization forced through the origin (eq. 7) (7) Figure 3: Parameterization of F CLD as a function of relative humidity and air temperature. (8) Parameterization of L: function of air temperature (T a ), vapor pressure (e a ) and relative humidity (RH) Water balance of the surface: simple bucket model to allow LUMPS to run over a range of seasonal and synoptic weather conditions (Offerle, 2003). Fraction of area with active vegetation: varies with vegetation phenology (V), parameterized combined growth and decay functions (Fig. 5, Table 3) Figure 4: RMSE performances of L options 2, 3 and 4 for the Baltimore (2001-2006) and Łódź (2001-2002) datasets. Vegetation phenology parameterizationNotation s start (s stop ) / f start (f stop ): start (stop) of leaf-on / leaf-off d s, d f : median point of spring / fall seasons k s, k f : coefficients characterizing the slope of growth and decay curves (Fig. 5) V 0 : transition window width coefficient 3.2: LUMPS evaluation: Łódź (2002) (Loridan et al. 2010) Table 3: Parameterization of active vegetation fraction in LUMPS Figure 5: Vegetation phenology for Łódź model runs Table 2: Parameterization of incoming longwave radiation in LUMPS Table 4: LUMPS parameter values used for Łódź runs (Fig. 5) Figure 6: Diurnal mean observed and modelled SEB fluxes for the December/ January/February (DJF), March/April/May (MAM), June/July/August (JJA) and September/October/November (SON) periods of 2002. Analysis of model error (Fig. 7, 8): Low temperatures (Fig. 8g): under prediction of sensible heat flux when nocturnal temperatures below 7 o C. Lack of explicit representation of anthropogenic heat. Wind direction (Fig. 8j,k) influence surface variability which is included in the measurements source area (Fig. 9). Important to have a better representation of flux footprint. Simple attempt to improve these aspects (Loridan et al. 2010; blue dashed lowess line, Fig. 7) Figure 9: Surface characteristics around the flux measurement tower in Łódź Figure 7: Evolution of the model error as a function of air temperature (a-d), hours after rain (e-h), wind direction (i-l) and wind speed (m-p) Figure 8: same as Fig. 7a-d and Fig. 7i-l but separating day (a-d, i-l) and night (e-h, m-p) time periods. Vaisala CL31 ceilometer operating at Kings College London since Nov 2006 Low-powered laser: samples the volume of air above, returning backscatter intensity of the atmosphere using the LIDAR principle 10 kHz backscatter sample frequency Cloud information backscatter profile generated every 15 s Each profile is classified as being either cloudy (C b = 1) or clear (C b = 0) Cloud height data post-processed to compute cloud fraction (F CLD ) using : Figure 2: (a) Example ceilometer backscatter plot with (b) estimated cloud cover for the same period where F CLD is cloud cover at measurement time t and w is the time window expressed as a number of data points. Here w = 30 (7.5 min). (9) Local-Scale Urban Meteorological Parameterization Scheme (LUMPS): longwave radiation parameterization and seasonality related developments Incoming longwave radiation is not routinely observed so needs to be modelled. (b) (a)